Reconstruction of Compressed Sensing MRI: An Experimental Review among Priors- and Deep Learning- based Algorithms

Magnetic resonance imaging (MRI) is the most effective and reliable imaging process to visualize the internal organs of the human body for clinical purposes and medical research. Although MRI does not use any radiation effect, it takes a long time to acquire an MRI image that makes discomfort to the patients, especially children and persons who have anxiety. Compressed sensing (CS) is a recently used technique for allowing faster acquisition of MRI. Since CS generates random under-sampling k-space measurements due to allowing faster acquisition, a non-linear algorithm is required for reconstructing a perfect MRI image. In this work, a comparative study is performed among single priors-, combined priors-, and deep learning-based non-linear algorithms for the reconstruction of MRI from CS measurements. Experiments are conducted to perform a comparative study that shows the significant aspects of the algorithms in terms of visual quality and quantitative measurements compared with each other. The results of the experiment proved the superiority of the combined priors-based method when compared with the single priors- and deep learning-based method.